Since the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits. The passage from the equations to the circuits was carried out directly without justification and subsequent formalisation. The present work shows a way to formally obtain the equivalence between an analogue circuit and a neural network and formalizes the connection between both systems. We also show which are the properties that these electrical networks must satisfy. We can have confidence that the representation in terms of circuits is mathematically equivalent to the equations that represent the network.
翻译:自20世纪80年代以来,尤其是在 Hopfield 模型的帮助下,递归神经网络或 RNN 成为一个备受关注的话题。神经网络的最初研究包括了由少量神经元组成的简单系统,通常通过模拟模拟模拟电路来实现。方程直接转化为电路,但没有进一步的证明和形式化。本文介绍了一种正式方法,可获得模拟电路和神经网络之间的等效性,并将这两个系统之间的连接正式化。我们还展示了这些电路网络必须满足的属性。我们可以确信,以电路形式的表示与代表网络的方程数学上是等效的。